import os import gradio as gr from datetime import datetime import pandas as pd import numpy as np import time #from sklearn.cluster import KMeans from sklearn.feature_extraction.text import CountVectorizer from transformers import AutoModel, AutoTokenizer from transformers.pipelines import pipeline from sklearn.pipeline import make_pipeline from sklearn.decomposition import TruncatedSVD from sklearn.feature_extraction.text import TfidfVectorizer import funcs.anonymiser as anon from umap import UMAP from torch import cuda, backends, version random_seed = 42 # Check for torch cuda print("Is CUDA enabled? ", cuda.is_available()) print("Is a CUDA device available on this computer?", backends.cudnn.enabled) if cuda.is_available(): torch_device = "gpu" print("Cuda version installed is: ", version.cuda) low_resource_mode = "No" #os.system("nvidia-smi") else: torch_device = "cpu" low_resource_mode = "Yes" print("Device used is: ", torch_device) #os.environ['CUDA_VISIBLE_DEVICES'] = '-1' from bertopic import BERTopic #from sentence_transformers import SentenceTransformer #from bertopic.backend._hftransformers import HFTransformerBackend #from cuml.manifold import UMAP #umap_model = UMAP(n_components=5, n_neighbors=15, min_dist=0.0) today = datetime.now().strftime("%d%m%Y") today_rev = datetime.now().strftime("%Y%m%d") from funcs.helper_functions import dummy_function, put_columns_in_df, read_file, get_file_path_end, zip_folder, delete_files_in_folder #from funcs.representation_model import representation_model from funcs.embeddings import make_or_load_embeddings # Log terminal output: https://github.com/gradio-app/gradio/issues/2362 import sys class Logger: def __init__(self, filename): self.terminal = sys.stdout self.log = open(filename, "w") def write(self, message): self.terminal.write(message) self.log.write(message) def flush(self): self.terminal.flush() self.log.flush() def isatty(self): return False sys.stdout = Logger("output.log") def read_logs(): sys.stdout.flush() with open("output.log", "r") as f: return f.read() # Load embeddings # Pinning a Jina revision for security purposes: https://www.baseten.co/blog/pinning-ml-model-revisions-for-compatibility-and-security/ # Save Jina model locally as described here: https://huggingface.co/jinaai/jina-embeddings-v2-base-en/discussions/29 embeddings_name = "jinaai/jina-embeddings-v2-small-en" # local_embeddings_location = "model/jina/" revision_choice = "b811f03af3d4d7ea72a7c25c802b21fc675a5d99" # Model used for representing topics hf_model_name = 'TheBloke/phi-2-orange-GGUF' #'NousResearch/Nous-Capybara-7B-V1.9-GGUF' # 'second-state/stablelm-2-zephyr-1.6b-GGUF' hf_model_file = 'phi-2-orange.Q5_K_M.gguf' #'Capybara-7B-V1.9-Q5_K_M.gguf' # 'stablelm-2-zephyr-1_6b-Q5_K_M.gguf' def save_topic_outputs(topic_model, data_file_name_no_ext, output_list, docs, save_topic_model, progress=gr.Progress()): topic_dets = topic_model.get_topic_info() if topic_dets.shape[0] == 1: topic_det_output_name = "topic_details_" + data_file_name_no_ext + "_" + today_rev + ".csv" topic_dets.to_csv(topic_det_output_name) output_list.append(topic_det_output_name) return output_list, "No topics found, original file returned" progress(0.8, desc= "Saving output") topic_det_output_name = "topic_details_" + data_file_name_no_ext + "_" + today_rev + ".csv" topic_dets.to_csv(topic_det_output_name) output_list.append(topic_det_output_name) doc_det_output_name = "doc_details_" + data_file_name_no_ext + "_" + today_rev + ".csv" doc_dets = topic_model.get_document_info(docs)[["Document", "Topic", "Name", "Representative_document"]] # "Probability", doc_dets.to_csv(doc_det_output_name) output_list.append(doc_det_output_name) topics_text_out_str = str(topic_dets["Name"]) output_text = "Topics: " + topics_text_out_str # Save topic model to file if save_topic_model == "Yes": topic_model_save_name_pkl = "output_model/" + data_file_name_no_ext + "_topics_" + today_rev + ".pkl"# + ".safetensors" topic_model_save_name_zip = topic_model_save_name_pkl + ".zip" # Clear folder before replacing files #delete_files_in_folder(topic_model_save_name_pkl) topic_model.save(topic_model_save_name_pkl, serialization='pickle', save_embedding_model=False, save_ctfidf=False) # Zip file example #zip_folder(topic_model_save_name_pkl, topic_model_save_name_zip) output_list.append(topic_model_save_name_pkl) return output_list, output_text def extract_topics(data, in_files, min_docs_slider, in_colnames, max_topics_slider, candidate_topics, in_label, anonymise_drop, return_intermediate_files, embeddings_super_compress, low_resource_mode, save_topic_model, embeddings_out, progress=gr.Progress()): progress(0, desc= "Loading data") if not in_colnames or not in_label: error_message = "Please enter one column name for the topics and another for the labelling." print(error_message) return error_message, None, None, embeddings_out all_tic = time.perf_counter() output_list = [] file_list = [string.name for string in in_files] data_file_names = [string.lower() for string in file_list if "tokenised" not in string and "npz" not in string.lower() and "gz" not in string.lower()] data_file_name = data_file_names[0] data_file_name_no_ext = get_file_path_end(data_file_name) in_colnames_list_first = in_colnames[0] if in_label: in_label_list_first = in_label[0] else: in_label_list_first = in_colnames_list_first # Make sure format of input series is good data[in_colnames_list_first] = data[in_colnames_list_first].fillna('').astype(str) data[in_label_list_first] = data[in_label_list_first].fillna('').astype(str) label_list = list(data[in_label_list_first]) if anonymise_drop == "Yes": progress(0.1, desc= "Anonymising data") anon_tic = time.perf_counter() data_anon_col, anonymisation_success = anon.anonymise_script(data, in_colnames_list_first, anon_strat="replace") data[in_colnames_list_first] = data_anon_col[in_colnames_list_first] anonymise_data_name = data_file_name_no_ext + "_anonymised_" + today_rev + ".csv" data.to_csv(anonymise_data_name) output_list.append(anonymise_data_name) anon_toc = time.perf_counter() time_out = f"Anonymising text took {anon_toc - anon_tic:0.1f} seconds" docs = list(data[in_colnames_list_first].str.lower()) # Check if embeddings are being loaded in progress(0.2, desc= "Loading/creating embeddings") print("Low resource mode: ", low_resource_mode) if low_resource_mode == "No": print("Using high resource Jina transformer model") try: embedding_model = AutoModel.from_pretrained(embeddings_name, revision = revision_choice, trust_remote_code=True,device_map="auto") except: embedding_model = AutoModel.from_pretrained(embeddings_name, revision = revision_choice, trust_remote_code=True, device_map="auto", use_auth_token=os.environ["HF_TOKEN"]) tokenizer = AutoTokenizer.from_pretrained(embeddings_name) embedding_model_pipe = pipeline("feature-extraction", model=embedding_model, tokenizer=tokenizer) # UMAP model uses Bertopic defaults umap_model = UMAP(n_neighbors=15, n_components=5, min_dist=0.0, metric='cosine', low_memory=False, random_state=random_seed) elif low_resource_mode == "Yes": print("Choosing low resource TF-IDF model.") embedding_model_pipe = make_pipeline( TfidfVectorizer(), TruncatedSVD(100) # 100 # To be compatible with zero shot, this needs to be lower than number of suggested topics ) embedding_model = embedding_model_pipe umap_model = TruncatedSVD(n_components=5, random_state=random_seed) embeddings_out, reduced_embeddings = make_or_load_embeddings(docs, file_list, embeddings_out, embedding_model, embeddings_super_compress, low_resource_mode) vectoriser_model = CountVectorizer(stop_words="english", ngram_range=(1, 2), min_df=0.1) progress(0.3, desc= "Embeddings loaded. Creating BERTopic model") if not candidate_topics: topic_model = BERTopic( embedding_model=embedding_model_pipe, vectorizer_model=vectoriser_model, umap_model=umap_model, min_topic_size = min_docs_slider, nr_topics = max_topics_slider, verbose = True) topics_text, probs = topic_model.fit_transform(docs, embeddings_out) # Do this if you have pre-defined topics else: if low_resource_mode == "Yes": error_message = "Zero shot topic modelling currently not compatible with low-resource embeddings. Please change this option to 'No' on the options tab and retry." print(error_message) return error_message, output_list, None, embeddings_out, data_file_name_no_ext, None, docs, label_list zero_shot_topics = read_file(candidate_topics.name) zero_shot_topics_lower = list(zero_shot_topics.iloc[:, 0].str.lower()) topic_model = BERTopic( embedding_model=embedding_model_pipe, vectorizer_model=vectoriser_model, umap_model=umap_model, min_topic_size = min_docs_slider, nr_topics = max_topics_slider, zeroshot_topic_list = zero_shot_topics_lower, zeroshot_min_similarity = 0.6, # 0.7 verbose = True) topics_text, probs = topic_model.fit_transform(docs, embeddings_out) if not topics_text: return "No topics found.", data_file_name, None, embeddings_out, data_file_name_no_ext, topic_model, docs, label_list else: print("Topic model created.") # Outputs output_list, output_text = save_topic_outputs(topic_model, data_file_name_no_ext, output_list, docs, save_topic_model) # If you want to save your embedding files if return_intermediate_files == "Yes": print("Saving embeddings to file") if low_resource_mode == "Yes": embeddings_file_name = data_file_name_no_ext + '_' + 'tfidf_embeddings.npz' else: if embeddings_super_compress == "No": embeddings_file_name = data_file_name_no_ext + '_' + 'jina_embeddings.npz' else: embeddings_file_name = data_file_name_no_ext + '_' + 'jina_embeddings_compress.npz' np.savez_compressed(embeddings_file_name, embeddings_out) output_list.append(embeddings_file_name) all_toc = time.perf_counter() time_out = f"All processes took {all_toc - all_tic:0.1f} seconds." print(time_out) return output_text, output_list, None, embeddings_out, data_file_name_no_ext, topic_model, docs, label_list def reduce_outliers(topic_model, docs, embeddings_out, data_file_name_no_ext, low_resource_mode, create_llm_topic_labels, save_topic_model, progress=gr.Progress()): #from funcs.prompts import capybara_prompt, capybara_start, open_hermes_prompt, open_hermes_start, stablelm_prompt, stablelm_start from funcs.representation_model import create_representation_model, llm_config, chosen_start_tag output_list = [] all_tic = time.perf_counter() vectoriser_model = CountVectorizer(stop_words="english", ngram_range=(1, 2), min_df=0.1) topics_text, probs = topic_model.fit_transform(docs, embeddings_out) #progress(0.2, desc= "Loading in representation model") #print("Create LLM topic labels:", create_llm_topic_labels) #representation_model = create_representation_model(create_llm_topic_labels, llm_config, hf_model_name, hf_model_file, chosen_start_tag, low_resource_mode) # Reduce outliers if required, then update representation progress(0.2, desc= "Reducing outliers") print("Reducing outliers.") # Calculate the c-TF-IDF representation for each outlier document and find the best matching c-TF-IDF topic representation using cosine similarity. topics_text = topic_model.reduce_outliers(docs, topics_text, strategy="embeddings") # Then, update the topics to the ones that considered the new data print("Finished reducing outliers.") progress(0.5, desc= "Creating topic representations") print("Create LLM topic labels:", "No") representation_model = create_representation_model("No", llm_config, hf_model_name, hf_model_file, chosen_start_tag, low_resource_mode) topic_model.update_topics(docs, topics=topics_text, vectorizer_model=vectoriser_model, representation_model=representation_model) topic_dets = topic_model.get_topic_info() # Replace original labels with LLM labels if "Phi" in topic_model.get_topic_info().columns: llm_labels = [label[0][0].split("\n")[0] for label in topic_model.get_topics(full=True)["Phi"].values()] topic_model.set_topic_labels(llm_labels) else: topic_model.set_topic_labels(list(topic_dets["Name"])) # Outputs output_list, output_text = save_topic_outputs(topic_model, data_file_name_no_ext, output_list, docs, save_topic_model) all_toc = time.perf_counter() time_out = f"All processes took {all_toc - all_tic:0.1f} seconds" print(time_out) return output_text, output_list, embeddings_out def represent_topics(topic_model, docs, embeddings_out, data_file_name_no_ext, low_resource_mode, save_topic_model, progress=gr.Progress()): #from funcs.prompts import capybara_prompt, capybara_start, open_hermes_prompt, open_hermes_start, stablelm_prompt, stablelm_start from funcs.representation_model import create_representation_model, llm_config, chosen_start_tag output_list = [] all_tic = time.perf_counter() vectoriser_model = CountVectorizer(stop_words="english", ngram_range=(1, 2), min_df=0.1) topics_text, probs = topic_model.fit_transform(docs, embeddings_out) topic_dets = topic_model.get_topic_info() progress(0.2, desc= "Creating topic representations") print("Create LLM topic labels:", "Yes") representation_model = create_representation_model("Yes", llm_config, hf_model_name, hf_model_file, chosen_start_tag, low_resource_mode) topic_model.update_topics(docs, topics=topics_text, vectorizer_model=vectoriser_model, representation_model=representation_model) # Replace original labels with LLM labels if "Phi" in topic_model.get_topic_info().columns: llm_labels = [label[0][0].split("\n")[0] for label in topic_model.get_topics(full=True)["Phi"].values()] topic_model.set_topic_labels(llm_labels) with open('llm_topic_list.txt', 'w') as file: for item in llm_labels: file.write(f"{item}\n") output_list.append('llm_topic_list.txt') else: topic_model.set_topic_labels(list(topic_dets["Name"])) # Outputs output_list, output_text = save_topic_outputs(topic_model, data_file_name_no_ext, output_list, docs, save_topic_model) all_toc = time.perf_counter() time_out = f"All processes took {all_toc - all_tic:0.1f} seconds" print(time_out) return output_text, output_list, embeddings_out def visualise_topics(topic_model, docs, data_file_name_no_ext, low_resource_mode, embeddings_out, label_list, sample_prop, visualisation_type_radio, progress=gr.Progress()): output_list = [] vis_tic = time.perf_counter() from funcs.bertopic_vis_documents import visualize_documents_custom topic_dets = topic_model.get_topic_info() # Replace original labels with LLM labels if "Phi" in topic_model.get_topic_info().columns: llm_labels = [label[0][0].split("\n")[0] for label in topic_model.get_topics(full=True)["Phi"].values()] topic_model.set_topic_labels(llm_labels) else: topic_model.set_topic_labels(list(topic_dets["Name"])) # Pre-reduce embeddings for visualisation purposes if low_resource_mode == "No": reduced_embeddings = UMAP(n_neighbors=15, n_components=2, min_dist=0.0, metric='cosine', random_state=random_seed).fit_transform(embeddings_out) else: reduced_embeddings = TruncatedSVD(2, random_state=random_seed).fit_transform(embeddings_out) progress(0.5, desc= "Creating visualisation (this can take a while)") # Visualise the topics: print("Creating visualisation") # "Topic document graph", "Hierarchical view" if visualisation_type_radio == "Topic document graph": topics_vis = visualize_documents_custom(topic_model, docs, hover_labels = label_list, reduced_embeddings=reduced_embeddings, hide_annotations=True, hide_document_hover=False, custom_labels=True, sample = sample_prop) topics_vis_name = data_file_name_no_ext + '_' + 'visualisation_' + today_rev + '.html' topics_vis.write_html(topics_vis_name) output_list.append(topics_vis_name) elif visualisation_type_radio == "Hierarchical view": hierarchical_topics = topic_model.hierarchical_topics(docs) topics_vis = topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings, sample = sample_prop) topics_vis_2 = topic_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics) topics_vis_name = data_file_name_no_ext + '_' + 'vis_hierarchy_topic_doc_' + today_rev + '.html' topics_vis.write_html(topics_vis_name) output_list.append(topics_vis_name) topics_vis_2_name = data_file_name_no_ext + '_' + 'vis_hierarchy_' + today_rev + '.html' topics_vis_2.write_html(topics_vis_2_name) output_list.append(topics_vis_2_name) # Save new hierarchical topic model to file import pandas as pd hierarchical_topics_name = data_file_name_no_ext + '_' + 'vis_hierarchy_topics' + today_rev + '.csv' hierarchical_topics.to_csv(hierarchical_topics_name) output_list.append(hierarchical_topics_name) #output_list, output_text = save_topic_outputs(topic_model, data_file_name_no_ext, output_list, docs, save_topic_model) all_toc = time.perf_counter() time_out = f"Creating visualisation took {all_toc - vis_tic:0.1f} seconds" print(time_out) return time_out, output_list, topics_vis, embeddings_out def save_as_pytorch_model(topic_model, docs, data_file_name_no_ext , progress=gr.Progress()): output_list = [] topic_model_save_name_folder = "output_model/" + data_file_name_no_ext + "_topics_" + today_rev# + ".safetensors" topic_model_save_name_zip = topic_model_save_name_folder + ".zip" # Clear folder before replacing files delete_files_in_folder(topic_model_save_name_folder) topic_model.save(topic_model_save_name_folder, serialization='pytorch', save_embedding_model=True, save_ctfidf=False) # Zip file example zip_folder(topic_model_save_name_folder, topic_model_save_name_zip) output_list.append(topic_model_save_name_zip) # Gradio app block = gr.Blocks(theme = gr.themes.Base()) with block: data_state = gr.State(pd.DataFrame()) embeddings_state = gr.State(np.array([])) topic_model_state = gr.State() docs_state = gr.State() data_file_name_no_ext_state = gr.State() label_list_state = gr.State() gr.Markdown( """ # Topic modeller Generate topics from open text in tabular data. Upload a file (csv, xlsx, or parquet), then specify the open text column that you want to use to generate topics, and another for labels in the visualisation. If you have an embeddings .npz file of the text made using the 'jina-embeddings-v2-small-en' model, you can load this in at the same time to skip the first modelling step. If you have a pre-defined list of topics, you can upload this as a csv file under 'I have my own list of topics...'. Further configuration options are available under the 'Options' tab. Suggested test dataset: https://huggingface.co/datasets/rag-datasets/mini_wikipedia/tree/main/data (passages.parquet) """) with gr.Tab("Load files and find topics"): with gr.Accordion("Load data file", open = True): in_files = gr.File(label="Input text from file", file_count="multiple") with gr.Row(): in_colnames = gr.Dropdown(choices=["Choose a column"], multiselect = True, label="Select column to find topics (first will be chosen if multiple selected).") in_label = gr.Dropdown(choices=["Choose a column"], multiselect = True, label="Select column for labelling documents in the output visualisation.") with gr.Accordion("I have my own list of topics (zero shot topic modelling).", open = False): candidate_topics = gr.File(label="Input topics from file (csv). File should have at least one column with a header and topic keywords in cells below. Topics will be taken from the first column of the file. Currently not compatible with low-resource embeddings.") with gr.Row(): min_docs_slider = gr.Slider(minimum = 2, maximum = 1000, value = 15, step = 1, label = "Minimum number of similar documents needed to make a topic.") max_topics_slider = gr.Slider(minimum = 2, maximum = 500, value = 10, step = 1, label = "Maximum number of topics") with gr.Row(): topics_btn = gr.Button("Extract topics") with gr.Row(): output_single_text = gr.Textbox(label="Output topics") output_file = gr.File(label="Output file") with gr.Accordion("Post processing options.", open = True): with gr.Row(): reduce_outliers_btn = gr.Button("Reduce outliers") represent_llm_btn = gr.Button("Generate topic labels with LLMs") logs = gr.Textbox(label="Processing logs.") with gr.Tab("Visualise"): sample_slide = gr.Slider(minimum = 0.01, maximum = 1, value = 0.1, step = 0.01, label = "Proportion of data points to show on output visualisation.") visualisation_type_radio = gr.Radio(choices=["Topic document graph", "Hierarchical view"]) plot_btn = gr.Button("Visualise topic model") out_plot_file = gr.File(label="Output plots to file", file_count="multiple") plot = gr.Plot(label="Visualise your topics here. Go to the 'Options' tab to enable.") with gr.Tab("Options"): with gr.Accordion("Data load and processing options", open = True): with gr.Row(): anonymise_drop = gr.Dropdown(value = "No", choices=["Yes", "No"], multiselect=False, label="Anonymise data on file load. Names and other details are replaced with tags e.g. ''.") embedding_super_compress = gr.Dropdown(label = "Round embeddings to three dp for smaller files with less accuracy.", value="No", choices=["Yes", "No"]) #create_llm_topic_labels = gr.Dropdown(label = "Create topic labels based on LLMs.", value="No", choices=["Yes", "No"]) with gr.Row(): low_resource_mode_opt = gr.Dropdown(label = "Use low resource embeddings and processing.", value="No", choices=["Yes", "No"]) return_intermediate_files = gr.Dropdown(label = "Return intermediate processing files from file preparation. Files can be loaded in to save processing time in future.", value="Yes", choices=["Yes", "No"]) save_topic_model = gr.Dropdown(label = "Save topic model to file.", value="Yes", choices=["Yes", "No"]) # Update column names dropdown when file uploaded in_files.upload(fn=put_columns_in_df, inputs=[in_files], outputs=[in_colnames, in_label, data_state, embeddings_state, output_single_text, topic_model_state]) in_colnames.change(dummy_function, in_colnames, None) topics_btn.click(fn=extract_topics, inputs=[data_state, in_files, min_docs_slider, in_colnames, max_topics_slider, candidate_topics, in_label, anonymise_drop, return_intermediate_files, embedding_super_compress, low_resource_mode_opt, save_topic_model, embeddings_state], outputs=[output_single_text, output_file, plot, embeddings_state, data_file_name_no_ext_state, topic_model_state, docs_state, label_list_state], api_name="topics") reduce_outliers_btn.click(fn=reduce_outliers, inputs=[topic_model_state, docs_state, embeddings_state, data_file_name_no_ext_state, low_resource_mode_opt], outputs=[output_single_text, output_file, embeddings_state], api_name="reduce_outliers") represent_llm_btn.click(fn=represent_topics, inputs=[topic_model_state, docs_state, embeddings_state, data_file_name_no_ext_state, low_resource_mode_opt], outputs=[output_single_text, output_file, embeddings_state], api_name="represent_llm") plot_btn.click(fn=visualise_topics, inputs=[topic_model_state, docs_state, data_file_name_no_ext_state, low_resource_mode_opt, embeddings_state, label_list_state, sample_slide, visualisation_type_radio], outputs=[output_single_text, out_plot_file, plot], api_name="plot") block.load(read_logs, None, logs, every=5) block.queue().launch(debug=True)#, server_name="0.0.0.0", ssl_verify=False, server_port=7860)